George Saeid’s Post

RIP long study hours AI can help you learn easily in minutes That too while using all AI models like GPT-4o, Claude, and Llama, all in one place for FREE Here’s how: Save this post for later --- Go to you.com . It is the best AI-powered search engine on the internet. --- What do you get on You? 1. Get detailed information on any topic 2. Create AI art 3. Do in-depth research on any topic 4. Solve problems 5. Use any LLM you want to --- How you can use it for learning? Here is an important use case: Try this prompt in Smart Mode: Understanding Complex Concepts You are an expert professor. I am [mention the problem you’re facing in detail with context]. You are and Explain the concept of [complex topic] in simple terms. Include real-world analogies, practical examples, and a step-by-step breakdown of the core principles. Assume the reader has no prior knowledge of the subject. Additionally, provide a list of resources for further reading and a few questions to test comprehension. I want you to [mention how you want the output in detail with examples]. --- Try using You here: you.com #ArtificialIntelligence #MachineLearning #DeepLearning #AI #AIResearch #AIEthics #AITechnology #AIInnovation #AIForGood #BigData #DataScience #NLP #ComputerVision #AITrends

Understanding RAGEval: Evaluating AI’s Knowledge in Different Scenarios Imagine you have an AI system that helps answer questions by pulling information from various sources, like documents or databases This type of system is known as Retrieval-Augmented Generation (RAG). While RAG systems are powerful, evaluating how well they perform in specific situations (like finance, healthcare, or law) is tricky That’s where RAGEval comes in. This new framework automatically creates test cases for RAG systems tailored to specific scenarios. Here’s a simple way to think about it: Setting the Scene: RAGEval starts with a small set of example documents to understand the key details like names, dates, and events that are important for that specific field. Generating Content: Based on these details, it generates a variety of new documents that stick to the facts while adding some variation to test how well the AI can handle different but related information. Question-Answer Tests: RAGEval then creates a set of questions and corresponding answers from these documents. The AI’s job is to find the right answers by pulling from the generated content. Evaluating Performance: Finally, it checks how well the AI did, focusing on whether the answers were complete, free of errors (hallucinations), and relevant. #AI #MachineLearning #ArtificialIntelligence #RAG #AIEvaluation #DataScience #TechInnovation #NaturalLanguageProcessing #AIResearch #DeepLearning #NLP #AIModels #TechTrends

🌟 Understanding Self-Attention vs. Multi-Head Self-Attention 🌟 In the world of deep learning, particularly with transformers, understanding the mechanisms of self-attention and multi-head self-attention is crucial for leveraging their full potential. Here’s a quick breakdown of the key differences: 🔍 Self-Attention: - Analyzes relationships between elements in a sequence. - Transforms each element into Query, Key, and Value vectors. - Computes compatibility scores to determine how much focus each element should have on others. - Creates a context-aware representation by weighting Value vectors. ✨ Multi-Head Self-Attention: - Runs multiple self-attention processes in parallel, allowing the model to focus on different aspects. - Projects input into multiple Q, K, and V vectors for each head. Calculates separate attention scores and outputs, then concatenates them for a richer representation. 💡 Analogy: - Self-attention is like understanding how each word in a sentence relates to others to grasp overall meaning. - Multi-head self-attention is akin to reading the sentence multiple times, focusing on different elements like grammar, context, and sentiment, and then combining insights for a deeper understanding. These mechanisms are foundational in tasks like machine translation, enabling models to capture long-range dependencies effectively. As we continue to innovate in deep learning, self-attention and multi-head self-attention will remain pivotal in advancing our capabilities in processing complex sequential data. #SelfAttention #MultiHeadSelfAttention #Transformers #DeepLearning #NLP #AI #MachineLearning

"Transforming Data with Feature Engineering: A Key to Model Success" 🔍 Feature Engineering: The Secret Sauce to Enhancing Your Models! Feature engineering is crucial for improving model performance. Here’s how you can leverage it: Create New Features: Derive features from existing ones (e.g., creating "age" from "birthdate"). Feature Selection: Choose the most relevant features to reduce noise (e.g., using correlation matrices or feature importance scores). Normalization & Scaling: Ensure your features are on a similar scale (e.g., Min-Max Scaling or Standardization). 💡 Quick Tip: Experiment with different feature transformations and selections to see what works best for your model. #AI #ML #DeepLearning #NLP #GenerativeAI #DataScience #MachineLearning #FeatureEngineering

I am thrilled to share that we will be presenting our paper at LREC-COLING 2024: "Analyzing Interpretability of Summarization Models with Eye-gaze Information". This paper explores how the interpretability 🔍 of summarization models compares to the human writing ✍ process by analyzing eye-gaze 👀 behavior. If you are passionate about human-AI alignment, Machine Learning Interpretability, and related areas, Let's connect and dive deep into these fascinating topics together! 📚✨ Looking forward to engaging discussions and insightful exchanges at the conference! #LRECCOLING2024 #AI #NLP #MachineLearning #HumanAIAlignment #MLInterpretability #EyeTracking

- Laying the Foundation - Prioritize understanding machine learning fundamentals like neural networks and backpropagation before delving into Generative AI. - Utilize resources like "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron for a solid grasp of ML concepts. - Getting Practical - Engage with specific generative models like GANs, VAEs, and Transformers by exploring foundational papers and tutorials on platforms like Coursera. - Experiment and learn through implementing models on platforms such as Google Colab for hands-on experience. - Accessing Tools - Make use of the vast library of pre-trained models available on platforms like Hugging Face for NLP tasks to streamline your learning and projects. Can you share your experience with implementing a pre-trained model in Generative AI?🌟 #GenerativeAI #MachineLearning #HuggingFace #NLP #ArtificialIntelligence Learn more about this topic here: https://lnkd.in/eAdjeDYu At Grow Scale Win™, we use AI to enhance productivity and efficiency. The AI Growth System® provides a holistic approach to business growth with a personalized strategy and AI-powered tools for lead generation, nurturing prospects, and closing sales. Learn more at www.growscale.win. DM us with any questions.

*Unlock the Power of Self-Attention in Transformers! 🔓* I'm thrilled to share my latest blog post, where I delve into the fascinating world of self-attention mechanisms in transformers! 🔍✨ Discover how self-attention works its magic, why it's a game-changer for modern NLP models, and how it drives state-of-the-art architectures. Read now and elevate your AI expertise! 🚀 *Check it out here:* ( https://lnkd.in/gkQ_PqXM ) #AI #MachineLearning #DeepLearning #Transformers #SelfAttention #NLP #ArtificialIntelligence #DataScience

Today’s learning journey takes me deeper into understanding Generative AI and Large Language Models (LLMs)! 💡 Let’s break it down together: 🤔 What is Generative AI? Generative AI creates new content like text, images, or even music by learning patterns from huge datasets. LLMs, a type of generative AI, are built to generate human-like text. 🔧 How are LLMs trained? The more parameters an LLM has, the better its memory and ability to perform complex tasks. Essentially, the larger the model, the more sophisticated tasks it can handle. Here’s where I need your thoughts! 📝 The text we input into the model is called a prompt. 🔍 The space for processing that input is the context window. ✍️ The generated output is called a completion. 🧠 The entire process of generating the text is known as inference. 💬 How do you think the size of the context window affects the quality of the model’s output? Let me know in the comments! #GenerativeAI #LLM #AI #DeepLearning #InteractiveLearning #MachineLearning #NLP #Innovation #DataScience #LearningTogether #Knowledge #Growth #AIResearch #TechTrends #DigitalTransformation #LifelongLearning #ProfessionalDevelopment

🔥How Generative AI sparks creativity? ☑️Generative AI sparks creativity by leveraging complex algorithms to autonomously generate new, diverse, and often unexpected outputs. ☑️By learning patterns and structures from vast datasets, these algorithms can produce novel content, whether it's images, text, or other forms of creative expression. ☑️The ability to combine and remix existing ideas in unique ways opens up new avenues for innovation and inspiration, fostering a dynamic interplay between human input and machine-generated output. ☑️Through this collaboration, Generative AI becomes a powerful tool for expanding creative boundaries and unlocking fresh perspectives in various fields. →Swipe through and discover the boundless possibilities of AI-generated wonders #datascience #artificialintelligence #machinelearning #deeplearning #nlp #generatieveai

I’m thrilled to announce that I have successfully completed the “Introduction to Prompt Engineering” certification! 🚀 This course has equipped me with valuable skills in crafting effective prompts for AI systems, enabling me to drive better outcomes in natural language processing, conversational AI, and other machine learning applications. From understanding the nuances of AI communication to mastering prompt optimization, this certification has provided deep insights into how we can make AI systems smarter and more responsive. I’m excited to apply these skills in future projects, leveraging prompt engineering to unlock the full potential of AI in real-world use cases. #PromptEngineering #AI #MachineLearning #ArtificialIntelligence #NLP #CareerGrowth #ContinuousLearning